Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations105034
Missing cells0
Missing cells (%)0.0%
Duplicate rows107
Duplicate rows (%)0.1%
Total size in memory22.4 MiB
Average record size in memory224.0 B

Variable types

Categorical3
Text2
DateTime2
Numeric21

Alerts

Dataset has 107 (0.1%) duplicate rowsDuplicates
Average_Rating is highly overall correlated with Hotel_Name and 5 other fieldsHigh correlation
Hotel_Name is highly overall correlated with Average_Rating and 8 other fieldsHigh correlation
Num_of_Ratings is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
breadth is highly overall correlated with depthHigh correlation
cleanliness_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
comfort_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
depth is highly overall correlated with breadthHigh correlation
employee_friendliness_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
facility_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
hotel_grade is highly overall correlated with Hotel_Name and 3 other fieldsHigh correlation
location_score is highly overall correlated with Hotel_NameHigh correlation
value_for_money_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
is_photo is highly imbalanced (71.9%) Imbalance
Helpfulness has 95776 (91.2%) zeros Zeros
Deviation of star ratings has 3010 (2.9%) zeros Zeros

Reproduction

Analysis started2025-01-14 13:55:27.035733
Analysis finished2025-01-14 13:57:06.772051
Duration1 minute and 39.74 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Hotel_Name
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size820.7 KiB
blakemore
 
4988
park-plaza-county-hall
 
4988
lancaster-gate
 
4982
thistletower
 
4982
milleniumgloucester
 
4976
Other values (28)
80118 

Length

Max length35
Median length22
Mean length15.897671
Min length3

Characters and Unicode

Total characters1669796
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstudios2let
2nd rowstudios2let
3rd rowstudios2let
4th rowstudios2let
5th rowstudios2let

Common Values

ValueCountFrequency (%)
blakemore 4988
 
4.7%
park-plaza-county-hall 4988
 
4.7%
lancaster-gate 4982
 
4.7%
thistletower 4982
 
4.7%
milleniumgloucester 4976
 
4.7%
stgileshotel 4973
 
4.7%
zedwell-trocaderor 4968
 
4.7%
z-trafalgar 4817
 
4.6%
marlin-waterloo 4423
 
4.2%
nyx-hotel-london-by-leonardo-hotels 3846
 
3.7%
Other values (23) 57091
54.4%

Length

2025-01-14T22:57:06.991943image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blakemore 4988
 
4.7%
park-plaza-county-hall 4988
 
4.7%
lancaster-gate 4982
 
4.7%
thistletower 4982
 
4.7%
milleniumgloucester 4976
 
4.7%
stgileshotel 4973
 
4.7%
zedwell-trocaderor 4968
 
4.7%
z-trafalgar 4817
 
4.6%
marlin-waterloo 4423
 
4.2%
nyx-hotel-london-by-leonardo-hotels 3846
 
3.7%
Other values (23) 57091
54.4%

Most occurring characters

ValueCountFrequency (%)
e 178106
10.7%
o 164498
 
9.9%
t 161718
 
9.7%
l 153189
 
9.2%
a 127848
 
7.7%
r 118084
 
7.1%
n 97466
 
5.8%
s 89698
 
5.4%
- 85943
 
5.1%
i 74030
 
4.4%
Other values (16) 419216
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1669796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 178106
10.7%
o 164498
 
9.9%
t 161718
 
9.7%
l 153189
 
9.2%
a 127848
 
7.7%
r 118084
 
7.1%
n 97466
 
5.8%
s 89698
 
5.4%
- 85943
 
5.1%
i 74030
 
4.4%
Other values (16) 419216
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1669796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 178106
10.7%
o 164498
 
9.9%
t 161718
 
9.7%
l 153189
 
9.2%
a 127848
 
7.7%
r 118084
 
7.1%
n 97466
 
5.8%
s 89698
 
5.4%
- 85943
 
5.1%
i 74030
 
4.4%
Other values (16) 419216
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1669796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 178106
10.7%
o 164498
 
9.9%
t 161718
 
9.7%
l 153189
 
9.2%
a 127848
 
7.7%
r 118084
 
7.1%
n 97466
 
5.8%
s 89698
 
5.4%
- 85943
 
5.1%
i 74030
 
4.4%
Other values (16) 419216
25.1%
Distinct93986
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:07.759775image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3534
Median length1872
Mean length205.16283
Min length1

Characters and Unicode

Total characters21549073
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93355 ?
Unique (%)88.9%

Sample

1st rowPerfect location with good connections and shops and pubs
2nd rowThe room had everything you needed Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing
3rd rowConveniently nearby St Pancras, very small but clean and pleasant room first floor with small balcony to street side Interesting areaLuggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests
4th rowReception staffed 24 hours a dayAll good
5th rowVery convenient to Kings Cross and the cityA little dated could do with a lick of paint
ValueCountFrequency (%)
the 212593
 
5.5%
and 145959
 
3.8%
was 122391
 
3.2%
to 97379
 
2.5%
a 90811
 
2.4%
room 73085
 
1.9%
in 64560
 
1.7%
very 53705
 
1.4%
for 50911
 
1.3%
of 49501
 
1.3%
Other values (69191) 2873059
74.9%
2025-01-14T22:57:08.784867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3749964
17.4%
e 2067368
 
9.6%
o 1596721
 
7.4%
t 1544636
 
7.2%
a 1483302
 
6.9%
n 1148704
 
5.3%
r 1050903
 
4.9%
i 1028863
 
4.8%
s 963141
 
4.5%
l 824293
 
3.8%
Other values (58) 6091178
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21549073
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3749964
17.4%
e 2067368
 
9.6%
o 1596721
 
7.4%
t 1544636
 
7.2%
a 1483302
 
6.9%
n 1148704
 
5.3%
r 1050903
 
4.9%
i 1028863
 
4.8%
s 963141
 
4.5%
l 824293
 
3.8%
Other values (58) 6091178
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21549073
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3749964
17.4%
e 2067368
 
9.6%
o 1596721
 
7.4%
t 1544636
 
7.2%
a 1483302
 
6.9%
n 1148704
 
5.3%
r 1050903
 
4.9%
i 1028863
 
4.8%
s 963141
 
4.5%
l 824293
 
3.8%
Other values (58) 6091178
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21549073
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3749964
17.4%
e 2067368
 
9.6%
o 1596721
 
7.4%
t 1544636
 
7.2%
a 1483302
 
6.9%
n 1148704
 
5.3%
r 1050903
 
4.9%
i 1028863
 
4.8%
s 963141
 
4.5%
l 824293
 
3.8%
Other values (58) 6091178
28.3%
Distinct1107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size820.7 KiB
Minimum2021-12-01 00:00:00
Maximum2024-12-13 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-14T22:57:08.982410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:57:09.173496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rating
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7462888
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:09.348927image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8802602
Coefficient of variation (CV)0.24273045
Kurtosis2.2232264
Mean7.7462888
Median Absolute Deviation (MAD)1
Skewness-1.3300718
Sum813623.7
Variance3.5353784
MonotonicityNot monotonic
2025-01-14T22:57:09.505153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 32154
30.6%
9 19382
18.5%
10 17620
16.8%
7 17356
16.5%
6 7226
 
6.9%
5 4339
 
4.1%
4 2404
 
2.3%
3 1822
 
1.7%
1 1769
 
1.7%
2 890
 
0.8%
Other values (15) 72
 
0.1%
ValueCountFrequency (%)
1 1769
1.7%
2 890
 
0.8%
2.5 1
 
< 0.1%
2.9 1
 
< 0.1%
3 1822
1.7%
3.8 1
 
< 0.1%
4 2404
2.3%
4.6 1
 
< 0.1%
5 4339
4.1%
5.4 2
 
< 0.1%
ValueCountFrequency (%)
10 17620
16.8%
9.6 15
 
< 0.1%
9.2 12
 
< 0.1%
9 19382
18.5%
8.8 7
 
< 0.1%
8.3 8
 
< 0.1%
8 32154
30.6%
7.9 7
 
< 0.1%
7.5 5
 
< 0.1%
7.1 3
 
< 0.1%

Average_Rating
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8544776
Minimum7
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:09.658580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17.6
median7.8
Q38.2
95-th percentile8.6
Maximum8.7
Range1.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.43647323
Coefficient of variation (CV)0.055569988
Kurtosis-0.50992273
Mean7.8544776
Median Absolute Deviation (MAD)0.3
Skewness0.027029348
Sum824987.2
Variance0.19050888
MonotonicityNot monotonic
2025-01-14T22:57:09.810893image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.7 23449
22.3%
7.4 9944
9.5%
7.8 9375
 
8.9%
8.4 8663
 
8.2%
7.9 8049
 
7.7%
8.3 7662
 
7.3%
7 6994
 
6.7%
8.6 6957
 
6.6%
7.6 5548
 
5.3%
8 5233
 
5.0%
Other values (5) 13160
12.5%
ValueCountFrequency (%)
7 6994
 
6.7%
7.1 2075
 
2.0%
7.4 9944
9.5%
7.5 2199
 
2.1%
7.6 5548
 
5.3%
7.7 23449
22.3%
7.8 9375
 
8.9%
7.9 8049
 
7.7%
8 5233
 
5.0%
8.1 4423
 
4.2%
ValueCountFrequency (%)
8.7 2607
 
2.5%
8.6 6957
 
6.6%
8.4 8663
 
8.2%
8.3 7662
 
7.3%
8.2 1856
 
1.8%
8.1 4423
 
4.2%
8 5233
 
5.0%
7.9 8049
 
7.7%
7.8 9375
 
8.9%
7.7 23449
22.3%

Num_of_Ratings
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11837.911
Minimum5613
Maximum39497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:09.977133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5613
5-th percentile5898
Q17132
median9767
Q313923
95-th percentile20956
Maximum39497
Range33884
Interquartile range (IQR)6791

Descriptive statistics

Standard deviation7251.4155
Coefficient of variation (CV)0.61255872
Kurtosis7.2443839
Mean11837.911
Median Absolute Deviation (MAD)3252
Skewness2.5857582
Sum1.2433831 × 109
Variance52583027
MonotonicityNot monotonic
2025-01-14T22:57:10.146923image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
12340 4988
 
4.7%
11045 4988
 
4.7%
20956 4982
 
4.7%
14445 4982
 
4.7%
15320 4976
 
4.7%
14989 4973
 
4.7%
39497 4968
 
4.7%
13923 4817
 
4.6%
10695 4423
 
4.2%
9394 3846
 
3.7%
Other values (24) 57091
54.4%
ValueCountFrequency (%)
5613 1903
1.8%
5715 1969
1.9%
5898 1856
1.8%
5932 2263
2.2%
5933 2424
2.3%
6120 2070
2.0%
6248 1756
1.7%
6277 2482
2.4%
6335 2139
2.0%
6404 2021
1.9%
ValueCountFrequency (%)
39497 4968
4.7%
20956 4982
4.7%
15320 4976
4.7%
14989 4973
4.7%
14445 4982
4.7%
13923 4817
4.6%
12641 3542
3.4%
12340 4988
4.7%
11670 3478
3.3%
11045 4988
4.7%

Helpfulness
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10283337
Minimum0
Maximum14
Zeros95776
Zeros (%)91.2%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:10.328444image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.36626649
Coefficient of variation (CV)3.5617475
Kurtosis60.791416
Mean0.10283337
Median Absolute Deviation (MAD)0
Skewness5.3616557
Sum10801
Variance0.13415114
MonotonicityNot monotonic
2025-01-14T22:57:10.482687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 95776
91.2%
1 8084
 
7.7%
2 939
 
0.9%
3 164
 
0.2%
4 39
 
< 0.1%
5 20
 
< 0.1%
6 7
 
< 0.1%
10 2
 
< 0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 95776
91.2%
1 8084
 
7.7%
2 939
 
0.9%
3 164
 
0.2%
4 39
 
< 0.1%
5 20
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 7
 
< 0.1%
5 20
 
< 0.1%
4 39
 
< 0.1%
3 164
 
0.2%
2 939
 
0.9%
1 8084
7.7%

is_photo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size820.7 KiB
0
99914 
1
 
5120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters105034
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99914
95.1%
1 5120
 
4.9%

Length

2025-01-14T22:57:10.666419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T22:57:11.131126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 99914
95.1%
1 5120
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 99914
95.1%
1 5120
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99914
95.1%
1 5120
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99914
95.1%
1 5120
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99914
95.1%
1 5120
 
4.9%
Distinct53217
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:11.825443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length120
Median length104
Mean length31.249291
Min length1

Characters and Unicode

Total characters3282238
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50754 ?
Unique (%)48.3%

Sample

1st rowExceptional
2nd rowVery good
3rd rowConvenient location
4th rowPeaceful position in an elegant street close to 3 major stations and the Bloomsbury area
5th rowGreat little gem in the city centre
ValueCountFrequency (%)
good 29477
 
5.2%
location 19829
 
3.5%
and 19725
 
3.5%
very 18806
 
3.3%
stay 17170
 
3.0%
great 16637
 
2.9%
a 16358
 
2.9%
for 13780
 
2.4%
the 13679
 
2.4%
hotel 12911
 
2.3%
Other values (11270) 393201
68.8%
2025-01-14T22:57:12.885021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
472368
14.4%
e 307605
 
9.4%
o 288012
 
8.8%
a 248364
 
7.6%
t 240166
 
7.3%
n 185082
 
5.6%
l 160008
 
4.9%
r 158081
 
4.8%
i 152253
 
4.6%
s 116322
 
3.5%
Other values (56) 953977
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3282238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
472368
14.4%
e 307605
 
9.4%
o 288012
 
8.8%
a 248364
 
7.6%
t 240166
 
7.3%
n 185082
 
5.6%
l 160008
 
4.9%
r 158081
 
4.8%
i 152253
 
4.6%
s 116322
 
3.5%
Other values (56) 953977
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3282238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
472368
14.4%
e 307605
 
9.4%
o 288012
 
8.8%
a 248364
 
7.6%
t 240166
 
7.3%
n 185082
 
5.6%
l 160008
 
4.9%
r 158081
 
4.8%
i 152253
 
4.6%
s 116322
 
3.5%
Other values (56) 953977
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3282238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
472368
14.4%
e 307605
 
9.4%
o 288012
 
8.8%
a 248364
 
7.6%
t 240166
 
7.3%
n 185082
 
5.6%
l 160008
 
4.9%
r 158081
 
4.8%
i 152253
 
4.6%
s 116322
 
3.5%
Other values (56) 953977
29.1%

hotel_grade
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size820.7 KiB
4
51016 
3
39617 
5
7259 
0
 
4968
2
 
2174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters105034
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 51016
48.6%
3 39617
37.7%
5 7259
 
6.9%
0 4968
 
4.7%
2 2174
 
2.1%

Length

2025-01-14T22:57:13.076962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T22:57:13.258363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4 51016
48.6%
3 39617
37.7%
5 7259
 
6.9%
0 4968
 
4.7%
2 2174
 
2.1%

Most occurring characters

ValueCountFrequency (%)
4 51016
48.6%
3 39617
37.7%
5 7259
 
6.9%
0 4968
 
4.7%
2 2174
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105034
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 51016
48.6%
3 39617
37.7%
5 7259
 
6.9%
0 4968
 
4.7%
2 2174
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105034
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 51016
48.6%
3 39617
37.7%
5 7259
 
6.9%
0 4968
 
4.7%
2 2174
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105034
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 51016
48.6%
3 39617
37.7%
5 7259
 
6.9%
0 4968
 
4.7%
2 2174
 
2.1%

employee_friendliness_score
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5368243
Minimum7.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:13.411268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile8
Q18.3
median8.6
Q38.7
95-th percentile9.1
Maximum9.1
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.36846077
Coefficient of variation (CV)0.04316134
Kurtosis0.58257582
Mean8.5368243
Median Absolute Deviation (MAD)0.2
Skewness-0.69152495
Sum896656.8
Variance0.13576334
MonotonicityNot monotonic
2025-01-14T22:57:13.571148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8.7 21523
20.5%
8.6 15566
14.8%
8.1 9941
9.5%
8.4 9534
9.1%
9.1 9393
8.9%
8.5 9050
8.6%
9 8600
 
8.2%
8.3 5677
 
5.4%
8 4976
 
4.7%
8.8 4539
 
4.3%
Other values (2) 6235
 
5.9%
ValueCountFrequency (%)
7.5 4096
 
3.9%
8 4976
 
4.7%
8.1 9941
9.5%
8.2 2139
 
2.0%
8.3 5677
 
5.4%
8.4 9534
9.1%
8.5 9050
8.6%
8.6 15566
14.8%
8.7 21523
20.5%
8.8 4539
 
4.3%
ValueCountFrequency (%)
9.1 9393
8.9%
9 8600
 
8.2%
8.8 4539
 
4.3%
8.7 21523
20.5%
8.6 15566
14.8%
8.5 9050
8.6%
8.4 9534
9.1%
8.3 5677
 
5.4%
8.2 2139
 
2.0%
8.1 9941
9.5%

facility_score
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8651351
Minimum6.9
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:13.752898image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile6.9
Q17.5
median7.8
Q38.3
95-th percentile8.7
Maximum8.7
Range1.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.50563122
Coefficient of variation (CV)0.064287671
Kurtosis-0.80302545
Mean7.8651351
Median Absolute Deviation (MAD)0.3
Skewness0.023523189
Sum826106.6
Variance0.25566293
MonotonicityNot monotonic
2025-01-14T22:57:13.937849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.8 12926
12.3%
7.5 12729
12.1%
8.7 11441
10.9%
7.6 9419
9.0%
8.3 8112
 
7.7%
6.9 6994
 
6.7%
8 6854
 
6.5%
7.4 4976
 
4.7%
7.2 4968
 
4.7%
8.4 4817
 
4.6%
Other values (7) 21798
20.8%
ValueCountFrequency (%)
6.9 6994
6.7%
7.2 4968
 
4.7%
7.3 2075
 
2.0%
7.4 4976
 
4.7%
7.5 12729
12.1%
7.6 9419
9.0%
7.7 4238
 
4.0%
7.8 12926
12.3%
7.9 2424
 
2.3%
8 6854
6.5%
ValueCountFrequency (%)
8.7 11441
10.9%
8.6 1969
 
1.9%
8.5 2674
 
2.5%
8.4 4817
 
4.6%
8.3 8112
7.7%
8.2 4423
 
4.2%
8.1 3995
 
3.8%
8 6854
6.5%
7.9 2424
 
2.3%
7.8 12926
12.3%

cleanliness_score
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2577404
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:14.119626image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.4
Q18
median8.2
Q38.7
95-th percentile8.8
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.43996909
Coefficient of variation (CV)0.053279599
Kurtosis-0.38967786
Mean8.2577404
Median Absolute Deviation (MAD)0.3
Skewness-0.20111935
Sum867343.5
Variance0.1935728
MonotonicityNot monotonic
2025-01-14T22:57:14.276883image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8.7 14785
14.1%
8.2 11855
11.3%
8.8 11508
11.0%
7.9 10516
10.0%
8.1 9667
9.2%
8.3 9060
8.6%
8 9046
8.6%
8.4 7837
7.5%
7.8 4976
 
4.7%
7.3 4973
 
4.7%
Other values (4) 10811
10.3%
ValueCountFrequency (%)
7.3 4973
4.7%
7.4 2021
 
1.9%
7.5 2075
 
2.0%
7.8 4976
4.7%
7.9 10516
10.0%
8 9046
8.6%
8.1 9667
9.2%
8.2 11855
11.3%
8.3 9060
8.6%
8.4 7837
7.5%
ValueCountFrequency (%)
9.1 4576
 
4.4%
8.8 11508
11.0%
8.7 14785
14.1%
8.5 2139
 
2.0%
8.4 7837
7.5%
8.3 9060
8.6%
8.2 11855
11.3%
8.1 9667
9.2%
8 9046
8.6%
7.9 10516
10.0%

comfort_score
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2560895
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:14.454239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.3
Q18
median8.2
Q38.7
95-th percentile8.9
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4652842
Coefficient of variation (CV)0.056356488
Kurtosis-0.56329281
Mean8.2560895
Median Absolute Deviation (MAD)0.3
Skewness-0.14122156
Sum867170.1
Variance0.21648939
MonotonicityNot monotonic
2025-01-14T22:57:14.623647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 13662
13.0%
7.9 12143
11.6%
8.2 11161
10.6%
8.8 10786
10.3%
8.1 9895
9.4%
8.9 8834
8.4%
7.3 6994
6.7%
8.3 6955
6.6%
8.5 5616
 
5.3%
8.7 4817
 
4.6%
Other values (6) 14171
13.5%
ValueCountFrequency (%)
7.3 6994
6.7%
7.4 2075
 
2.0%
7.8 3478
 
3.3%
7.9 12143
11.6%
8 13662
13.0%
8.1 9895
9.4%
8.2 11161
10.6%
8.3 6955
6.6%
8.4 1903
 
1.8%
8.5 5616
5.3%
ValueCountFrequency (%)
9.1 2607
 
2.5%
9 1969
 
1.9%
8.9 8834
8.4%
8.8 10786
10.3%
8.7 4817
4.6%
8.6 2139
 
2.0%
8.5 5616
5.3%
8.4 1903
 
1.8%
8.3 6955
6.6%
8.2 11161
10.6%

value_for_money_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7125198
Minimum7
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:14.801430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.3
Q17.4
median7.7
Q37.9
95-th percentile8.2
Maximum8.3
Range1.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.32423694
Coefficient of variation (CV)0.042040338
Kurtosis-0.69560853
Mean7.7125198
Median Absolute Deviation (MAD)0.2
Skewness-0.18014129
Sum810076.8
Variance0.10512959
MonotonicityNot monotonic
2025-01-14T22:57:14.952617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.9 24703
23.5%
7.4 14225
13.5%
7.5 11117
10.6%
8.1 10536
10.0%
7.6 10104
9.6%
7.7 9196
 
8.8%
7.3 7175
 
6.8%
7 4973
 
4.7%
8.2 4817
 
4.6%
8 4363
 
4.2%
ValueCountFrequency (%)
7 4973
 
4.7%
7.3 7175
 
6.8%
7.4 14225
13.5%
7.5 11117
10.6%
7.6 10104
9.6%
7.7 9196
 
8.8%
7.9 24703
23.5%
8 4363
 
4.2%
8.1 10536
10.0%
8.2 4817
 
4.6%
ValueCountFrequency (%)
8.3 3825
 
3.6%
8.2 4817
 
4.6%
8.1 10536
10.0%
8 4363
 
4.2%
7.9 24703
23.5%
7.7 9196
 
8.8%
7.6 10104
9.6%
7.5 11117
10.6%
7.4 14225
13.5%
7.3 7175
 
6.8%

location_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1724051
Minimum8.2
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:15.120253image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile8.6
Q19
median9.1
Q39.4
95-th percentile9.6
Maximum9.7
Range1.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.31021043
Coefficient of variation (CV)0.033819966
Kurtosis0.42763495
Mean9.1724051
Median Absolute Deviation (MAD)0.2
Skewness-0.49813675
Sum963414.4
Variance0.096230509
MonotonicityNot monotonic
2025-01-14T22:57:15.265879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8.9 16753
16.0%
9.1 15117
14.4%
9 14049
13.4%
9.3 11747
11.2%
9.4 11327
10.8%
9.5 9970
9.5%
9.6 7575
7.2%
9.2 6028
 
5.7%
8.6 5576
 
5.3%
9.7 4817
 
4.6%
ValueCountFrequency (%)
8.2 2075
 
2.0%
8.6 5576
 
5.3%
8.9 16753
16.0%
9 14049
13.4%
9.1 15117
14.4%
9.2 6028
 
5.7%
9.3 11747
11.2%
9.4 11327
10.8%
9.5 9970
9.5%
9.6 7575
7.2%
ValueCountFrequency (%)
9.7 4817
 
4.6%
9.6 7575
7.2%
9.5 9970
9.5%
9.4 11327
10.8%
9.3 11747
11.2%
9.2 6028
 
5.7%
9.1 15117
14.4%
9 14049
13.4%
8.9 16753
16.0%
8.6 5576
 
5.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size820.7 KiB
Minimum2024-12-02 00:00:00
Maximum2024-12-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-14T22:57:15.425668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:57:15.584216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

title_word_count
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4815679
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:15.751705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q38
95-th percentile16
Maximum31
Range30
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1027253
Coefficient of variation (CV)0.93088792
Kurtosis1.7959518
Mean5.4815679
Median Absolute Deviation (MAD)3
Skewness1.4452381
Sum575751
Variance26.037805
MonotonicityNot monotonic
2025-01-14T22:57:15.916406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 26575
25.3%
2 18035
17.2%
4 7451
 
7.1%
5 6930
 
6.6%
7 6697
 
6.4%
6 6636
 
6.3%
3 5242
 
5.0%
8 4448
 
4.2%
9 3844
 
3.7%
10 3187
 
3.0%
Other values (20) 15989
15.2%
ValueCountFrequency (%)
1 26575
25.3%
2 18035
17.2%
3 5242
 
5.0%
4 7451
 
7.1%
5 6930
 
6.6%
6 6636
 
6.3%
7 6697
 
6.4%
8 4448
 
4.2%
9 3844
 
3.7%
10 3187
 
3.0%
ValueCountFrequency (%)
31 1
 
< 0.1%
29 2
 
< 0.1%
28 7
 
< 0.1%
27 27
 
< 0.1%
26 48
 
< 0.1%
25 131
 
0.1%
24 253
 
0.2%
23 722
0.7%
22 402
0.4%
21 558
0.5%

text_word_count
Real number (ℝ)

Distinct419
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.732096
Minimum1
Maximum666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:16.128035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median24
Q348
95-th percentile112
Maximum666
Range665
Interquartile range (IQR)37

Descriptive statistics

Standard deviation40.637592
Coefficient of variation (CV)1.1063238
Kurtosis15.930849
Mean36.732096
Median Absolute Deviation (MAD)16
Skewness3.0589415
Sum3858119
Variance1651.4138
MonotonicityNot monotonic
2025-01-14T22:57:16.313581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3087
 
2.9%
7 3043
 
2.9%
6 2975
 
2.8%
11 2826
 
2.7%
9 2714
 
2.6%
4 2681
 
2.6%
8 2598
 
2.5%
12 2559
 
2.4%
10 2470
 
2.4%
14 2388
 
2.3%
Other values (409) 77693
74.0%
ValueCountFrequency (%)
1 1590
1.5%
2 2066
2.0%
3 2133
2.0%
4 2681
2.6%
5 3087
2.9%
6 2975
2.8%
7 3043
2.9%
8 2598
2.5%
9 2714
2.6%
10 2470
2.4%
ValueCountFrequency (%)
666 1
< 0.1%
568 1
< 0.1%
527 1
< 0.1%
510 1
< 0.1%
503 1
< 0.1%
493 1
< 0.1%
491 1
< 0.1%
479 1
< 0.1%
471 1
< 0.1%
469 1
< 0.1%

Deviation of star ratings
Real number (ℝ)

Zeros 

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3003513
Minimum0
Maximum7.7
Zeros3010
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:16.539975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.4
median1
Q31.7
95-th percentile3.9
Maximum7.7
Range7.7
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2705356
Coefficient of variation (CV)0.97707101
Kurtosis5.5055816
Mean1.3003513
Median Absolute Deviation (MAD)0.6
Skewness2.126962
Sum136581.1
Variance1.6142606
MonotonicityNot monotonic
2025-01-14T22:57:16.758451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 9684
 
9.2%
0.4 8717
 
8.3%
0.3 7140
 
6.8%
1.4 6464
 
6.2%
1 4595
 
4.4%
1.3 4584
 
4.4%
0.7 4302
 
4.1%
0.1 4274
 
4.1%
1.6 3310
 
3.2%
0.3 3235
 
3.1%
Other values (92) 48729
46.4%
ValueCountFrequency (%)
0 3010
 
2.9%
0.1 4274
4.1%
0.2 612
 
0.6%
0.2 2678
 
2.5%
0.3 7140
6.8%
0.3 3235
 
3.1%
0.4 8717
8.3%
0.5 945
 
0.9%
0.6 9684
9.2%
0.6 2
 
< 0.1%
ValueCountFrequency (%)
7.7 17
 
< 0.1%
7.6 18
 
< 0.1%
7.4 105
 
0.1%
7.3 60
 
0.1%
7.2 9
 
< 0.1%
7.1 71
 
0.1%
7 92
 
0.1%
6.9 125
 
0.1%
6.8 169
 
0.2%
6.7 490
0.5%

Time_lapsed
Real number (ℝ)

Distinct1100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.35697
Minimum-2
Maximum1097
Zeros73
Zeros (%)0.1%
Negative29
Negative (%)< 0.1%
Memory size820.7 KiB
2025-01-14T22:57:17.270882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile33
Q1199
median504
Q3804
95-th percentile1018
Maximum1097
Range1099
Interquartile range (IQR)605

Descriptive statistics

Standard deviation330.30957
Coefficient of variation (CV)0.65361634
Kurtosis-1.2577181
Mean505.35697
Median Absolute Deviation (MAD)303
Skewness0.06327195
Sum53079664
Variance109104.41
MonotonicityNot monotonic
2025-01-14T22:57:17.458862image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 1141
 
1.1%
33 935
 
0.9%
34 909
 
0.9%
41 901
 
0.9%
39 874
 
0.8%
21 752
 
0.7%
35 705
 
0.7%
42 519
 
0.5%
38 497
 
0.5%
40 448
 
0.4%
Other values (1090) 97353
92.7%
ValueCountFrequency (%)
-2 2
 
< 0.1%
-1 27
 
< 0.1%
0 73
0.1%
1 72
0.1%
2 103
0.1%
3 102
0.1%
4 81
0.1%
5 59
0.1%
6 83
0.1%
7 122
0.1%
ValueCountFrequency (%)
1097 3
 
< 0.1%
1096 42
 
< 0.1%
1095 54
 
0.1%
1094 59
 
0.1%
1093 80
0.1%
1092 158
0.2%
1091 91
0.1%
1090 81
0.1%
1089 80
0.1%
1088 70
0.1%

depth
Real number (ℝ)

High correlation 

Distinct87256
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-38.450017
Minimum-100
Maximum-6.6776313
Zeros0
Zeros (%)0.0%
Negative105034
Negative (%)100.0%
Memory size820.7 KiB
2025-01-14T22:57:17.678987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-81.630696
Q1-48.067611
median-30.846572
Q3-26.462228
95-th percentile-9.6299307
Maximum-6.6776313
Range93.322369
Interquartile range (IQR)21.605383

Descriptive statistics

Standard deviation22.34745
Coefficient of variation (CV)-0.5812078
Kurtosis-0.30967033
Mean-38.450017
Median Absolute Deviation (MAD)16.409655
Skewness-0.5972004
Sum-4038559.1
Variance499.4085
MonotonicityNot monotonic
2025-01-14T22:57:17.886014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-100 1709
 
1.6%
-80.77214163 625
 
0.6%
-61.89359724 222
 
0.2%
-63.27307509 215
 
0.2%
-43.97077893 213
 
0.2%
-81.70272046 213
 
0.2%
-47.12967584 213
 
0.2%
-28.78978672 213
 
0.2%
-32.95383147 213
 
0.2%
-26.84931981 213
 
0.2%
Other values (87246) 100985
96.1%
ValueCountFrequency (%)
-100 1709
1.6%
-84.08841039 3
 
< 0.1%
-84.06961335 3
 
< 0.1%
-84.02995893 15
 
< 0.1%
-83.98996359 14
 
< 0.1%
-83.9396462 6
 
< 0.1%
-83.8907046 1
 
< 0.1%
-83.88746931 1
 
< 0.1%
-83.87837342 1
 
< 0.1%
-83.85918865 1
 
< 0.1%
ValueCountFrequency (%)
-6.677631348 1
< 0.1%
-7.054551847 1
< 0.1%
-7.054678819 1
< 0.1%
-7.142210643 1
< 0.1%
-7.143129074 1
< 0.1%
-7.154919742 1
< 0.1%
-7.156877476 1
< 0.1%
-7.157150862 1
< 0.1%
-7.172092309 1
< 0.1%
-7.177415895 1
< 0.1%

breadth
Real number (ℝ)

High correlation 

Distinct82895
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93646351
Minimum0.005777392
Maximum3.1635121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:18.113055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.005777392
5-th percentile0.21807606
Q10.53520752
median0.84586931
Q31.2483223
95-th percentile2.0762689
Maximum3.1635121
Range3.1577347
Interquartile range (IQR)0.7131148

Descriptive statistics

Standard deviation0.54585415
Coefficient of variation (CV)0.58288886
Kurtosis1.0469049
Mean0.93646351
Median Absolute Deviation (MAD)0.34053387
Skewness0.98541076
Sum98360.509
Variance0.29795676
MonotonicityNot monotonic
2025-01-14T22:57:18.303715image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.444466301 4851
 
4.6%
0.116197045 1709
 
1.6%
2.537805323 734
 
0.7%
2.433590432 610
 
0.6%
2.611251484 335
 
0.3%
1.912345451 223
 
0.2%
2.240900849 215
 
0.2%
0.2834890754 213
 
0.2%
1.137610053 213
 
0.2%
1.356611384 213
 
0.2%
Other values (82885) 95718
91.1%
ValueCountFrequency (%)
0.005777392036 1
< 0.1%
0.01009623009 1
< 0.1%
0.01321811738 1
< 0.1%
0.01409448027 1
< 0.1%
0.01597436954 1
< 0.1%
0.01643207731 1
< 0.1%
0.01672007141 1
< 0.1%
0.01699992329 1
< 0.1%
0.01869460668 1
< 0.1%
0.019387203 1
< 0.1%
ValueCountFrequency (%)
3.163512096 189
0.2%
3.158293468 1
 
< 0.1%
3.155762546 1
 
< 0.1%
3.153193764 1
 
< 0.1%
3.152642407 1
 
< 0.1%
3.152350184 1
 
< 0.1%
3.147265029 1
 
< 0.1%
3.147265029 2
 
< 0.1%
3.142907989 1
 
< 0.1%
3.142907989 1
 
< 0.1%

Topic_1
Real number (ℝ)

Distinct83415
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36742805
Minimum3.5965538 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:18.513479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.5965538 × 10-20
5-th percentile1.4274902 × 10-19
Q10.1390849
median0.293424
Q30.54108722
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.40200232

Descriptive statistics

Standard deviation0.29402731
Coefficient of variation (CV)0.80023097
Kurtosis-0.42612621
Mean0.36742805
Median Absolute Deviation (MAD)0.18666354
Skewness0.76895425
Sum38592.438
Variance0.086452058
MonotonicityNot monotonic
2025-01-14T22:57:18.735754image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5290
 
5.0%
0.2 1709
 
1.6%
5.917545801 × 10-20625
 
0.6%
4.375405103 × 10-20222
 
0.2%
7.421832538 × 10-20215
 
0.2%
0.4810026219 213
 
0.2%
0.1872919792 213
 
0.2%
0.5520064088 213
 
0.2%
0.3641658808 213
 
0.2%
0.2062364306 213
 
0.2%
Other values (83405) 95908
91.3%
ValueCountFrequency (%)
3.596553841 × 10-202
 
< 0.1%
3.628870637 × 10-201
 
< 0.1%
3.725090228 × 10-204
< 0.1%
3.800828335 × 10-207
< 0.1%
3.811613347 × 10-202
 
< 0.1%
3.816584352 × 10-205
< 0.1%
3.852417237 × 10-201
 
< 0.1%
3.862745713 × 10-201
 
< 0.1%
3.956788504 × 10-201
 
< 0.1%
3.995255687 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 5290
5.0%
1 6
 
< 0.1%
1 29
 
< 0.1%
1 6
 
< 0.1%
0.9999970167 1
 
< 0.1%
0.9999720444 1
 
< 0.1%
0.9999689049 1
 
< 0.1%
0.9999618554 1
 
< 0.1%
0.9999566256 1
 
< 0.1%
0.9999164213 1
 
< 0.1%

Topic_2
Real number (ℝ)

Distinct87212
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17220449
Minimum4.1036497 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:18.941889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.1036497 × 10-20
5-th percentile1.4327214 × 10-19
Q15.3751425 × 10-19
median0.096891182
Q30.26885167
95-th percentile0.62231571
Maximum1
Range1
Interquartile range (IQR)0.26885167

Descriptive statistics

Standard deviation0.21477365
Coefficient of variation (CV)1.2472012
Kurtosis2.4746093
Mean0.17220449
Median Absolute Deviation (MAD)0.096891182
Skewness1.5760945
Sum18087.326
Variance0.046127722
MonotonicityNot monotonic
2025-01-14T22:57:19.147180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1709
 
1.6%
1 734
 
0.7%
0.4264496132 222
 
0.2%
0.03029043695 215
 
0.2%
0.2339717256 213
 
0.2%
5.043365743 × 10-19213
 
0.2%
1.379980084 × 10-19213
 
0.2%
0.1400968446 213
 
0.2%
2.224823439 × 10-19213
 
0.2%
0.1468614751 213
 
0.2%
Other values (87202) 100876
96.0%
ValueCountFrequency (%)
4.103649693 × 10-201
< 0.1%
4.210130309 × 10-201
< 0.1%
4.324796118 × 10-201
< 0.1%
4.392074266 × 10-201
< 0.1%
4.534029116 × 10-201
< 0.1%
4.553730147 × 10-201
< 0.1%
4.595471696 × 10-201
< 0.1%
4.605588726 × 10-201
< 0.1%
4.703225091 × 10-201
< 0.1%
4.744443792 × 10-201
< 0.1%
ValueCountFrequency (%)
1 734
0.7%
0.9999660152 1
 
< 0.1%
0.9997226479 1
 
< 0.1%
0.9995902487 1
 
< 0.1%
0.9993696575 1
 
< 0.1%
0.9993453356 4
 
< 0.1%
0.999257843 1
 
< 0.1%
0.9988687418 1
 
< 0.1%
0.9986998072 2
 
< 0.1%
0.9983341549 10
 
< 0.1%

Topic_3
Real number (ℝ)

Distinct86957
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1851042
Minimum3.8008283 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:19.342840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.8008283 × 10-20
5-th percentile1.228761 × 10-19
Q14.6432844 × 10-19
median0.092248042
Q30.30267829
95-th percentile0.66629959
Maximum1
Range1
Interquartile range (IQR)0.30267829

Descriptive statistics

Standard deviation0.22982954
Coefficient of variation (CV)1.2416225
Kurtosis1.6944784
Mean0.1851042
Median Absolute Deviation (MAD)0.092248042
Skewness1.4410046
Sum19442.235
Variance0.052821616
MonotonicityNot monotonic
2025-01-14T22:57:19.547664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1709
 
1.6%
1 749
 
0.7%
5.917545801 × 10-20625
 
0.6%
4.375405103 × 10-20222
 
0.2%
0.9697095631 215
 
0.2%
0.2473001367 213
 
0.2%
0.3702786513 213
 
0.2%
1.160765191 × 10-19213
 
0.2%
2.178354194 × 10-18213
 
0.2%
0.4787782173 213
 
0.2%
Other values (86947) 100449
95.6%
ValueCountFrequency (%)
3.800828335 × 10-207
 
< 0.1%
3.816584352 × 10-205
 
< 0.1%
3.914987695 × 10-203
 
< 0.1%
3.994864839 × 10-201
 
< 0.1%
4.125028745 × 10-201
 
< 0.1%
4.212184738 × 10-201
 
< 0.1%
4.372526507 × 10-201
 
< 0.1%
4.375405103 × 10-20222
0.2%
4.451542865 × 10-201
 
< 0.1%
4.459429445 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 749
0.7%
0.9998358534 1
 
< 0.1%
0.9991329009 1
 
< 0.1%
0.9990908319 1
 
< 0.1%
0.9989812566 1
 
< 0.1%
0.9987648272 1
 
< 0.1%
0.9983753464 1
 
< 0.1%
0.9983033245 1
 
< 0.1%
0.9982702229 1
 
< 0.1%
0.9982696844 1
 
< 0.1%

Topic_4
Real number (ℝ)

Distinct87113
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16365715
Minimum3.9567885 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:19.760750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.9567885 × 10-20
5-th percentile1.0326366 × 10-19
Q13.0457348 × 10-19
median0.039925406
Q30.27877944
95-th percentile0.630764
Maximum1
Range1
Interquartile range (IQR)0.27877944

Descriptive statistics

Standard deviation0.22316053
Coefficient of variation (CV)1.3635856
Kurtosis1.8494859
Mean0.16365715
Median Absolute Deviation (MAD)0.039925406
Skewness1.5279897
Sum17189.565
Variance0.049800623
MonotonicityNot monotonic
2025-01-14T22:57:19.975181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1709
 
1.6%
5.917545801 × 10-20625
 
0.6%
1 335
 
0.3%
4.375405103 × 10-20222
 
0.2%
7.421832538 × 10-20215
 
0.2%
0.1461096444 213
 
0.2%
0.5189973781 213
 
0.2%
5.043365743 × 10-19213
 
0.2%
3.057680396 × 10-19213
 
0.2%
6.546896719 × 10-20213
 
0.2%
Other values (87103) 100863
96.0%
ValueCountFrequency (%)
3.956788504 × 10-201
 
< 0.1%
4.122943041 × 10-2015
 
< 0.1%
4.171045866 × 10-2041
 
< 0.1%
4.290890561 × 10-201
 
< 0.1%
4.294888776 × 10-205
 
< 0.1%
4.300264128 × 10-202
 
< 0.1%
4.307747075 × 10-201
 
< 0.1%
4.309640623 × 10-2013
 
< 0.1%
4.375405103 × 10-20222
0.2%
4.417898051 × 10-205
 
< 0.1%
ValueCountFrequency (%)
1 335
0.3%
0.9999701 1
 
< 0.1%
0.999787829 1
 
< 0.1%
0.9997040181 1
 
< 0.1%
0.9995311431 1
 
< 0.1%
0.9994995506 1
 
< 0.1%
0.9992841411 1
 
< 0.1%
0.9990260276 1
 
< 0.1%
0.9989358397 1
 
< 0.1%
0.9988747281 1
 
< 0.1%

Topic_5
Real number (ℝ)

Distinct87189
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11160611
Minimum4.2026183 × 10-20
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size820.7 KiB
2025-01-14T22:57:20.422636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.2026183 × 10-20
5-th percentile1.1049278 × 10-19
Q12.9879536 × 10-19
median0.016742258
Q30.17320165
95-th percentile0.51101472
Maximum1
Range1
Interquartile range (IQR)0.17320165

Descriptive statistics

Standard deviation0.18158961
Coefficient of variation (CV)1.627058
Kurtosis3.7945188
Mean0.11160611
Median Absolute Deviation (MAD)0.016742258
Skewness1.9786279
Sum11722.436
Variance0.032974787
MonotonicityNot monotonic
2025-01-14T22:57:20.620440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1709
 
1.6%
5.917545801 × 10-20625
 
0.6%
0.5735503868 222
 
0.2%
7.421832538 × 10-20215
 
0.2%
0.2575811153 213
 
0.2%
5.043365743 × 10-19213
 
0.2%
2.224823439 × 10-19213
 
0.2%
0.4477486579 213
 
0.2%
3.057680396 × 10-19213
 
0.2%
0.5187281377 213
 
0.2%
Other values (87179) 100985
96.1%
ValueCountFrequency (%)
4.202618322 × 10-201
 
< 0.1%
4.32538039 × 10-209
< 0.1%
4.338018924 × 10-201
 
< 0.1%
4.341911209 × 10-2010
< 0.1%
4.47889989 × 10-201
 
< 0.1%
4.489706285 × 10-201
 
< 0.1%
4.496296361 × 10-202
 
< 0.1%
4.526068888 × 10-206
< 0.1%
4.543972018 × 10-202
 
< 0.1%
4.626559691 × 10-201
 
< 0.1%
ValueCountFrequency (%)
1 189
0.2%
0.9996103673 1
 
< 0.1%
0.9994455937 1
 
< 0.1%
0.9991601075 1
 
< 0.1%
0.9991090511 1
 
< 0.1%
0.9990818294 1
 
< 0.1%
0.9985920573 1
 
< 0.1%
0.9985920573 2
 
< 0.1%
0.9981515196 1
 
< 0.1%
0.9981515196 1
 
< 0.1%

Interactions

2025-01-14T22:57:01.325329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:43.219029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:47.021220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:50.902892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:55.375874image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:58.794318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:03.097697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:07.432949image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:11.481777image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:15.497386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:19.281724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:23.448989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:26.926475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:30.567130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:34.569479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:38.255774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:42.014078image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:45.842914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:49.804003image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:53.512312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:57.669834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2025-01-14T22:55:46.893439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:50.688325image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:55.171813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:55:58.606033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:02.900614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:07.244086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:11.282255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:15.342788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:19.094448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:23.269576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:26.796830image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:30.393238image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:34.391464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:38.091980image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:41.872976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:45.660945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:49.667192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:53.342795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:56:57.450483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-01-14T22:57:01.153652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-01-14T22:57:20.831182image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Average_RatingDeviation of star ratingsHelpfulnessHotel_NameNum_of_RatingsRatingTime_lapsedTopic_1Topic_2Topic_3Topic_4Topic_5breadthcleanliness_scorecomfort_scoredepthemployee_friendliness_scorefacility_scorehotel_gradeis_photolocation_scoretext_word_counttitle_word_countvalue_for_money_score
Average_Rating1.000-0.021-0.0311.000-0.3000.305-0.028-0.081-0.0570.112-0.0210.053-0.0080.9220.8890.0440.8450.9480.4920.0860.180-0.0470.0250.719
Deviation of star ratings-0.0211.000-0.0350.192-0.120-0.0280.0480.064-0.0640.049-0.050-0.0100.0060.0110.004-0.0560.0360.0080.1290.049-0.0700.035-0.0750.011
Helpfulness-0.031-0.0351.0000.032-0.002-0.0780.0480.058-0.0110.0000.018-0.026-0.050-0.018-0.0170.010-0.037-0.0240.0100.021-0.0070.1290.014-0.016
Hotel_Name1.0000.1920.0321.0001.0000.1670.1680.0690.0700.0800.0980.0620.0581.0001.0000.0791.0001.0001.0000.1441.0000.0420.0651.000
Num_of_Ratings-0.300-0.120-0.0021.0001.000-0.078-0.0480.0020.074-0.083-0.0350.0210.024-0.357-0.241-0.001-0.303-0.3160.5920.1100.3440.0170.026-0.363
Rating0.305-0.028-0.0780.167-0.0781.000-0.023-0.2850.0400.208-0.0620.1770.0080.2880.2870.1630.2720.2960.1290.0800.081-0.199-0.0060.212
Time_lapsed-0.0280.0480.0480.168-0.048-0.0231.000-0.0090.0060.015-0.0010.0070.0040.0010.0090.0080.036-0.0300.0980.0260.027-0.053-0.0480.057
Topic_1-0.0810.0640.0580.0690.002-0.285-0.0091.000-0.293-0.230-0.136-0.137-0.225-0.058-0.065-0.298-0.084-0.0710.0400.041-0.0060.2750.021-0.034
Topic_2-0.057-0.064-0.0110.0700.0740.0400.006-0.2931.000-0.206-0.150-0.007-0.190-0.075-0.0780.300-0.057-0.0740.0530.0360.133-0.0450.040-0.061
Topic_30.1120.0490.0000.080-0.0830.2080.015-0.230-0.2061.000-0.142-0.016-0.2830.1110.1120.2930.1220.1140.0520.041-0.0460.0720.0260.087
Topic_4-0.021-0.0500.0180.098-0.035-0.062-0.001-0.136-0.150-0.1421.000-0.121-0.271-0.027-0.0230.319-0.014-0.0190.0520.040-0.1220.139-0.000-0.025
Topic_50.053-0.010-0.0260.0620.0210.1770.007-0.137-0.007-0.016-0.1211.000-0.2190.0440.0470.4130.0240.0490.0270.0580.0670.0010.0360.023
breadth-0.0080.006-0.0500.0580.0240.0080.004-0.225-0.190-0.283-0.271-0.2191.000-0.008-0.009-0.595-0.007-0.0080.0190.0440.005-0.428-0.101-0.007
cleanliness_score0.9220.011-0.0181.000-0.3570.2880.001-0.058-0.0750.111-0.0270.044-0.0081.0000.9610.0330.8500.9510.5310.0820.114-0.0430.0180.756
comfort_score0.8890.004-0.0171.000-0.2410.2870.009-0.065-0.0780.112-0.0230.047-0.0090.9611.0000.0420.8210.9430.4680.0600.133-0.0360.0230.664
depth0.044-0.0560.0100.079-0.0010.1630.008-0.2980.3000.2930.3190.413-0.5950.0330.0421.0000.0360.0380.0280.1040.0280.3360.1090.014
employee_friendliness_score0.8450.036-0.0371.000-0.3030.2720.036-0.084-0.0570.122-0.0140.024-0.0070.8500.8210.0361.0000.8210.3940.0970.144-0.0570.0210.704
facility_score0.9480.008-0.0241.000-0.3160.296-0.030-0.071-0.0740.114-0.0190.049-0.0080.9510.9430.0380.8211.0000.6540.0880.096-0.0410.0160.704
hotel_grade0.4920.1290.0101.0000.5920.1290.0980.0400.0530.0520.0520.0270.0190.5310.4680.0280.3940.6541.0000.0530.4120.0180.0240.331
is_photo0.0860.0490.0210.1440.1100.0800.0260.0410.0360.0410.0400.0580.0440.0820.0600.1040.0970.0880.0531.0000.0720.0890.1090.075
location_score0.180-0.070-0.0071.0000.3440.0810.027-0.0060.133-0.046-0.1220.0670.0050.1140.1330.0280.1440.0960.4120.0721.000-0.0050.0420.011
text_word_count-0.0470.0350.1290.0420.017-0.199-0.0530.275-0.0450.0720.1390.001-0.428-0.043-0.0360.336-0.057-0.0410.0180.089-0.0051.0000.218-0.038
title_word_count0.025-0.0750.0140.0650.026-0.006-0.0480.0210.0400.026-0.0000.036-0.1010.0180.0230.1090.0210.0160.0240.1090.0420.2181.0000.016
value_for_money_score0.7190.011-0.0161.000-0.3630.2120.057-0.034-0.0610.087-0.0250.023-0.0070.7560.6640.0140.7040.7040.3310.0750.011-0.0380.0161.000

Missing values

2025-01-14T22:57:05.326298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-14T22:57:06.071620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_word_counttext_word_countDeviation of star ratingsTime_lapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5
0studios2letPerfect location with good connections and shops and pubs2024-05-0110.07.61167000Exceptional38.37.57.97.87.69.32024-12-02192.4215-62.8409611.5741751.316480e-195.064535e-011.316480e-194.935465e-011.316480e-19
1studios2letThe room had everything you needed Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing2024-12-028.07.61167000Very good38.37.57.97.87.69.32024-12-022480.40-46.6316110.8392836.196515e-011.466066e-022.422931e-193.656879e-012.422931e-19
2studios2letConveniently nearby St Pancras, very small but clean and pleasant room first floor with small balcony to street side Interesting areaLuggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests2024-12-018.07.61167000Convenient location38.37.57.97.87.69.32024-12-022460.41-47.5887820.9795478.814442e-011.272794e-021.058278e-013.584634e-193.584634e-19
3studios2letReception staffed 24 hours a dayAll good2024-12-019.07.61167000Peaceful position in an elegant street close to 3 major stations and the Bloomsbury area38.37.57.97.87.69.32024-12-021571.41-47.4343892.1882554.358937e-022.553959e-191.493003e-029.414806e-012.553959e-19
4studios2letVery convenient to Kings Cross and the cityA little dated could do with a lick of paint2024-11-308.07.61167000Great little gem in the city centre38.37.57.97.87.69.32024-12-027170.42-31.3349240.4657555.737028e-012.578224e-011.090429e-021.575705e-018.609959e-19
5studios2letLocated in a quiet area but close to Kings Cross station so getting around was easy Several little pubs nearby for dining and some good coffee shops tooThere is no lift so dragging a heavy suitcase up and down stairs was challenging We had booked a room with terrace but the outdoor space was really minuscule not what we had expected from the photos2024-11-307.07.61167000Convenient, quiet location38.37.57.97.87.69.32024-12-023650.62-29.7405550.7426235.153979e-014.424249e-022.533275e-194.327751e-017.584540e-03
6studios2letIts spacious, good value and so very quiet for LondonYou sometimes have to wriggle the loo flusher to stop it running and running2024-11-309.07.61167000Superb38.37.57.97.87.69.32024-12-021231.42-30.1222971.7526415.167609e-021.280752e-022.931973e-198.345609e-011.009555e-01
7studios2letLocationLot of stairs bad knee2024-11-299.07.61167000Ideal location for travelling round38.37.57.97.87.69.32024-12-02551.43-66.6270611.3683949.888613e-011.113870e-022.160308e-182.160308e-182.160308e-18
8studios2letLocation was great, so near the stationWe were on the top floor, six flights of stairs and no lift\r\nHeating was on 247 full temperature and no means of reducing it2024-11-297.07.61167000Perfect location,38.37.57.97.87.69.32024-12-022310.63-45.4202641.3442911.112383e-013.912240e-011.786172e-191.786172e-194.975377e-01
9studios2letThe location which is excellent for public transport and local dining \r\nFriendly staffed reception where we could leave our travel bags all day after checking outThe climb up 3 flights of stairs was exhausting but it was our choice\r\nIt was a small room and the kitchen facilities were very sparse but we didnt need them2024-11-288.07.61167000Ideal accommodation for a short stay in London near St Pancreas station38.37.57.97.87.69.32024-12-0212570.44-45.3849170.5415053.454275e-013.039768e-013.505957e-012.074788e-192.074788e-19
Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_word_counttext_word_countDeviation of star ratingsTime_lapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5
105024montanahotelPerfect Location2023-09-0210.07.8624800Lovely staff39.07.78.28.28.09.42024-12-16222.2471-46.7541472.2691971.033399e-029.640266e-012.563936e-021.131889e-191.131889e-19
105025montanahotelIt was good enoughDelayed check in, cracked basin in bathroom, water pressure poor Everything else was fine2023-07-116.07.8624800Not a bad place to stay if its where you need to be39.07.78.28.28.09.42024-12-1613171.8524-63.5188371.5890802.217791e-012.387427e-192.387427e-197.782209e-012.387427e-19
105026montanahotellocationhot room, shower didnt drain, broken sink2023-07-015.07.8624800Great location and generally clean spot but the place is a bit a dated and the basement room was damp, hot and a bit mus39.07.78.28.28.09.42024-12-162572.8534-81.6901181.4444661.000000e+004.899119e-194.899119e-194.899119e-194.899119e-19
105027montanahotelGood to have teacoffee and a fridge in the roomThe building is beautiful but the interior decor leaves a lot to be desired The hotel is Indian in styleredgoldfaded wallpaper and threadbare carpetsthe room was OK but again needed updating We were in the basement with no viewonly rubbish out of the window There was no hot breakfast so only cereals, fruit and pastriesbut it was in a pleasant locationnear the tube and shopspubs etconly a short walk to the Natural History museum2023-04-256.07.8624800Lovely building with quite a grand entrancelet down by the interiorfine for overnight stay39.07.78.28.28.09.42024-12-1614831.8601-12.8278860.8312133.941411e-012.698174e-021.367020e-025.492943e-011.591269e-02
105028montanahotellocationwater pressure was non existent\r\ndespite several request to address the problem2023-03-253.07.8624800while the staff was nice They did very little to remedy the lack of shower and hot water problem we had39.07.78.28.28.09.42024-12-1622124.8632-82.5937141.4444661.000000e+003.923866e-183.923866e-183.923866e-183.923866e-18
105029montanahotelConvenient and classy The staff are excellent people, and Light of India is a fantastic restaurant I would certainly stay againNA2022-12-2810.07.8624800Highly recommend this little gem situated in my favourite part of town39.07.78.28.28.09.42024-12-1612212.2719-29.8596001.3062451.265215e-019.282940e-027.760552e-012.346284e-194.593887e-03
105030montanahotellovely atmosphere, extremely friendly and helpful staff2022-07-0110.07.8624800Perfect location for our visit to the Royal Albert Hall and the Natural History Museum would39.07.78.28.28.09.42024-12-161672.2899-64.4620332.3801921.392950e-191.392950e-199.932582e-011.392950e-196.741808e-03
105031montanahotelIt was a single room, a little small but it was fine for 1 person, it had everything I needed2022-06-2810.07.8624801The staff were very friendly and helpful The position was perfect for sightseeing39.07.78.28.28.09.42024-12-1613202.2902-65.1791671.3781509.906346e-019.365421e-033.743725e-193.743725e-193.743725e-19
105032montanahotelVery clean and well maintainedThe rooms are very nice and comfortable with staffs professionalismThe food are delicious,nice breakfast,lunch ,dinner and the cocktails are exceptionalNotting much just that theres no parking2022-02-1610.07.8624801Myself and my wife really enjoy our stay at this hotel,we love the service and all the staffs are amazingLooking forwar39.07.78.28.28.09.42024-12-1621302.21034-11.4260780.3128443.872025e-011.449441e-021.893646e-013.497771e-015.916139e-02
105033montanahotelThe staff were very friendly and helpful Especially Kampas The hotel was very clean and the fact that they had a wonderful Indian restaurant as part of it was amazing Best Vindaloo everShower a tad small but adequate xx2022-02-0610.07.8624801Loved every minute we will be back Xxx39.07.78.28.28.09.42024-12-168392.21044-30.4715301.4953431.831851e-011.796622e-038.082126e-011.521662e-196.805659e-03

Duplicate rows

Most frequently occurring

Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_word_counttext_word_countDeviation of star ratingsTime_lapseddepthbreadthTopic_1Topic_2Topic_3Topic_4Topic_5# duplicates
65park-plaza-county-hallBrilliant location on the South Bank opposite County Hall so great for events there I was upgraded to a room with an incredible view over Waterloo Station and to the Shard beyond Rooms are soundproof so no sound from the trains The staff I encountered were all very friendly from my welcome at check in to smiles from any other staff member I sawWould be good to offer glass bottles of water rather than the small plastic bottles2024-11-1110.08.61104500Great location, friendly staff and amazing views49.08.78.88.97.99.52024-12-027801.421-9.4407160.2834891.412377e-011.400968e-013.702787e-011.461096e-012.022772e-01213
66park-plaza-county-hallFree upgrade, Excellent customer service, beautiful room2024-11-1010.08.61104500Wonderful staff49.08.78.88.97.99.52024-12-02271.422-28.7897870.5424443.641659e-015.885294e-024.787782e-012.807232e-199.820296e-02213
67park-plaza-county-hallFriendly staff Excellent serviceNothing2024-11-119.08.61104500From every perspective a highly satisfactory stay49.08.78.88.97.99.52024-12-02740.421-63.2730752.2409017.421833e-203.029044e-029.697096e-017.421833e-207.421833e-20213
68park-plaza-county-hallGreat location, excellent attentive staff and great customer careNothing it was perfect2024-11-1010.08.61104501From start to finish our stay was perfect we were well looked after and had a fabulous room with a view Would definitel49.08.78.88.97.99.52024-12-0223121.422-43.9707791.3566116.546897e-202.339717e-012.473001e-016.546897e-205.187281e-01213
70park-plaza-county-hallLocationNo parking place in front2024-11-108.08.61104500It was good only faced problems in getting taxi49.08.78.88.97.99.52024-12-02950.622-32.9538310.6203325.520064e-019.748856e-032.178354e-181.806636e-012.575811e-01213
74park-plaza-county-hallThe hotel was ideally located for us We were upgraded to a studio room and given a bottle of wine Nice touch Breakfast was plentiful and good choicesThere was a mouse running around on Thursday evening in the bar Very tiny but a bit unsettling with food being around Didnt like they took 310 pending transaction without telling me I didnt owe a penny at all A bit much to check my card worked Said they have reversed it but not seen it yet2024-11-109.08.61104510Superb49.08.78.88.97.99.52024-12-021840.422-63.2972781.0510664.810026e-012.224823e-192.224823e-195.189974e-012.224823e-19213
77park-plaza-county-hallThe reception area, the lift views and our room was exceptional2024-11-0710.08.61104500We stayed with our daughter as we had tickets to ABBA voyage49.08.78.88.97.99.52024-12-0212111.425-81.7027201.4444661.000000e+005.043366e-195.043366e-195.043366e-195.043366e-19213
81park-plaza-county-hallVery close to some of the attractions like the London Eye, Westminister Abbey etc Rooms were spacious and neat I took the room which had a sofacum bed which gave us the extra space, especially with a kid Perfect for a family of 3We landed a bit early around 1030 While the checkin time was 1500 hrs, we requested if we could get the room a bit earlier I was ready to pay for an extra day too A person at reception counter mentioned that the room will get cleaned, but was very rude while he was talking When I tried checking after an hour to get an ETA, he simply denied to give an ETA He said it could take 30 mins or 2 hours, which I felt very rude However another wonderful lady at the reception was more helpful and got the room ready in 3040 mins2024-11-098.08.61104500Neat comfortable place with major attractions nearby49.08.78.88.97.99.52024-12-0281490.623-47.1296760.9535688.840022e-012.693799e-028.905982e-023.057680e-193.057680e-19213
83park-plaza-county-hallVery good hotel, spacious room with a great view Look forward to staying here again in future2024-11-1110.08.61104500Exceptional49.08.78.88.97.99.52024-12-021171.421-44.9338201.1376101.872920e-011.379980e-191.379980e-193.649594e-014.477487e-01213
85park-plaza-county-halllocation is great, quite, safe and close to restaurants, coffee shops, close to London eye easy to reach\r\nfast check in out, great front desk team\r\nroom is spacious and beds are comfortablethe cleaning service is not good comparing it to my previous stay\r\nsome furniture need to be renewed2024-11-108.08.61104500Great Stay49.08.78.88.97.99.52024-12-022520.622-26.8493200.7780922.062364e-011.468615e-011.160765e-191.826575e-014.642446e-01213